<?xml version="1.0" encoding="UTF-8"?>
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    <title>gpu</title>
    <link rel="self" type="application/atom+xml" href="https://links.biapy.com/guest/tags/147/feed"/>
    <updated>2026-06-18T08:07:09+00:00</updated>
    <id>https://links.biapy.com/guest/tags/147/feed</id>
            <entry>
            <id>https://links.biapy.com/links/12742</id>
            <title type="text"><![CDATA[ffmpeg-over-ip]]></title>
            <link rel="alternate" href="https://ffmpeg-over-ip.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/12742"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[GPU-accelerated ffmpeg without GPU passthrough.

Use GPU-accelerated ffmpeg from anywhere — a Docker container, a VM, or a remote machine — without GPU passthrough or shared filesystems.

- [ffmpeg-over-ip @ GitHub](https://github.com/steelbrain/ffmpeg-over-ip).

Related contents:

- [ffmpeg-over-ip - Le transcodage GPU distant pour Jellyfin @ Korben :fr:](https://korben.info/ffmpeg-over-ip-le-transcodage-gpu-distant-pour-jellyfin.html).]]>
            </summary>
            <updated>2026-05-15T08:24:26+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10980</id>
            <title type="text"><![CDATA[GPU Hot]]></title>
            <link rel="alternate" href="https://psalias2006.github.io/gpu-hot/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10980"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Real-time NVIDIA GPU Monitoring.

Real-time NVIDIA GPU monitoring dashboard. Web-based, no SSH required.

- [GPU Hot @ GitHub](https://github.com/psalias2006/gpu-hot).

Related contents:

- [Best Docker Apps of October 2025! @ ServersatHome&amp;#039;s YouTube](https://www.youtube.com/watch?v=dMhUiJqohFU).]]>
            </summary>
            <updated>2025-11-16T16:33:02+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10772</id>
            <title type="text"><![CDATA[gpu-io]]></title>
            <link rel="alternate" href="https://apps.amandaghassaei.com/gpu-io/examples/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10772"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[gpu-io is a GPU-accelerated computing library for physics simulations and other mathematical calculations.

- [gpu-io @ GitHub](https://github.com/amandaghassaei/gpu-io).]]>
            </summary>
            <updated>2025-10-27T13:23:03+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10564</id>
            <title type="text"><![CDATA[Tile Language (tile-lang)]]></title>
            <link rel="alternate" href="https://tilelang.com/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10564"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Domain-specific language designed to streamline the development of high-performance GPU/CPU/Accelerators kernels 

Tile Language (tile-lang) is a concise domain-specific language designed to streamline the development of high-performance GPU/CPU kernels (e.g., GEMM, Dequant GEMM, FlashAttention, LinearAttention). By employing a Pythonic syntax with an underlying compiler infrastructure on top of TVM, tile-lang allows developers to focus on productivity without sacrificing the low-level optimizations necessary for state-of-the-art performance.

- [Tile Language @ GitHub](https://github.com/tile-ai/tilelang).]]>
            </summary>
            <updated>2025-10-07T05:59:10+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10237</id>
            <title type="text"><![CDATA[StringZilla]]></title>
            <link rel="alternate" href="https://github.com/ashvardanian/StringZilla" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10237"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Up to 100x faster strings for C, C++, CUDA, Python, Rust, Swift, JS, &amp;amp; Go, leveraging NEON, AVX2, AVX-512, SVE, GPGPU, &amp;amp; SWAR to accelerate search, hashing, sorting, edit distances, sketches, and memory ops 🦖 

Related contents:

- [StringWa.rs on GPUs: Databases &amp;amp; Bioinformatics 🦠 @ Ash&amp;#039;s Blog](https://ashvardanian.com/posts/stringwars-on-gpus/).]]>
            </summary>
            <updated>2025-09-16T12:11:19+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/10215</id>
            <title type="text"><![CDATA[HAMi]]></title>
            <link rel="alternate" href="https://project-hami.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/10215"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Open, Device Virtualization, VGPU, Heterogeneous AI Computing.

HAMi (Heterogeneous AI Computing Virtualization Middleware) formerly known as k8s-vGPU-scheduler, is an &amp;#039;all-in-one&amp;#039; chart designed to manage Heterogeneous AI Computing Devices in a k8s cluster. It can provide the ability to share Heterogeneous AI devices and provide resource isolation among tasks.

- [HAMi @ GitHub](https://github.com/Project-HAMi/HAMi).]]>
            </summary>
            <updated>2025-09-15T14:01:05+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/48</id>
            <title type="text"><![CDATA[LACT]]></title>
            <link rel="alternate" href="https://github.com/ilya-zlobintsev/LACT" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/48"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Linux GPU Configuration And Monitoring Tool.

This application allows you to control your AMD, Nvidia or Intel GPU on a Linux system.

Related contents:

- [LACT - Le panneau de contrôle GPU qui manquait à Linux @ Korben :fr:](https://korben.info/lact-controle-gpu-amd-linux.html).]]>
            </summary>
            <updated>2026-02-02T09:00:09+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/277</id>
            <title type="text"><![CDATA[Rust GPU]]></title>
            <link rel="alternate" href="https://rust-gpu.github.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/277"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[🐉 Making Rust a first-class language and ecosystem for GPU shaders 🚧 

- [Rust GPU @ GitHub](https://github.com/rust-gpu/rust-gpu).

Related contents:

- [Rust running on every GPU @ Rust GPU](https://rust-gpu.github.io/blog/2025/07/25/rust-on-every-gpu/).]]>
            </summary>
            <updated>2025-12-31T14:19:24+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/466</id>
            <title type="text"><![CDATA[Sirius]]></title>
            <link rel="alternate" href="https://github.com/sirius-db/sirius" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/466"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Sirius is a GPU-native SQL engine. It plugs into existing databases such as DuckDB via the standard Substrait query format, requiring no query rewrites or major system changes. Sirius currently supports DuckDB and Doris (coming soon), other systems marked with * are on our roadmap.]]>
            </summary>
            <updated>2025-08-28T17:14:54+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/500</id>
            <title type="text"><![CDATA[Mojo 🔥]]></title>
            <link rel="alternate" href="https://www.modular.com/mojo" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/500"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Powerful CPU+GPU Programming.
Mojo   is a pythonic language for blazing-fast CPU+GPU execution without CUDA. Optionally use it with MAX for insanely fast AI inference.

- [Modular Platform @ GitHub](https://github.com/modular/modular).

Related contents:

- [Python can run Mojo now @  koaning.io](https://koaning.io/posts/giving-mojo-a-spin/).]]>
            </summary>
            <updated>2025-08-28T17:21:57+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1194</id>
            <title type="text"><![CDATA[Vello]]></title>
            <link rel="alternate" href="https://linebender.org/vello/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1194"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[A GPU compute-centric 2D renderer. 

Vello is a 2D graphics rendering engine written in Rust, with a focus on GPU compute. It can draw large 2D scenes with interactive or near-interactive performance, using wgpu for GPU access.

- [Vello @ GitHub](https://github.com/linebender/vello).

Related content:

- [I want a good parallel computer @ Raph Levien’s blog](https://raphlinus.github.io/gpu/2025/03/21/good-parallel-computer.html).]]>
            </summary>
            <updated>2025-08-28T19:15:02+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1683</id>
            <title type="text"><![CDATA[RunPod]]></title>
            <link rel="alternate" href="https://www.runpod.io/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1683"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[The Cloud Built for AI.

Train, fine-tune and deploy AI models with RunPod.

Globally distributed GPU cloud for your AI workloads.
Deploy any GPU workload seamlessly, so you can focus less on
infrastructure and more on running ML models.]]>
            </summary>
            <updated>2025-08-28T20:36:40+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1684</id>
            <title type="text"><![CDATA[Vast.ai]]></title>
            <link rel="alternate" href="https://vast.ai/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1684"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Rent GPUs.

Vast.ai is the market leader in low-cost cloud GPU rental.
Use one simple interface to save 5-6X on GPU compute.]]>
            </summary>
            <updated>2025-08-28T20:36:40+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/1970</id>
            <title type="text"><![CDATA[GPU Glossary]]></title>
            <link rel="alternate" href="https://modal.com/gpu-glossary/readme" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/1970"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[We wrote this glossary to solve a problem we ran into working with GPUs here at Modal : the documentation is fragmented, making it difficult to connect concepts at different levels of the stack, like Streaming Multiprocessor Architecture , Compute Capability , and nvcc compiler flags .]]>
            </summary>
            <updated>2025-08-28T21:24:22+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/2278</id>
            <title type="text"><![CDATA[Metal Puzzles]]></title>
            <link rel="alternate" href="https://github.com/abeleinin/Metal-Puzzles" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/2278"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Solve Puzzles. Learn Metal 🤘.

Port of srush/GPU-Puzzles to Metal using MLX Custom Kernals. 

GPUs are crucial in machine learning because they can process data on a massively parallel scale. While it&amp;#039;s possible to become an expert in machine learning without writing any GPU code, building intuition is challenging when you&amp;#039;re only working through layers of abstraction. Additionally, as models grow in complexity, the need for developers to write efficient, high-performance kernels becomes increasingly important to leverage the power of modern hardware.]]>
            </summary>
            <updated>2025-08-28T22:16:41+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5629</id>
            <title type="text"><![CDATA[nvitop]]></title>
            <link rel="alternate" href="https://github.com/XuehaiPan/nvitop" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5629"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[An interactive NVIDIA-GPU process viewer and beyond, the one-stop solution for GPU process management.]]>
            </summary>
            <updated>2025-08-29T07:36:12+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/5931</id>
            <title type="text"><![CDATA[GPU.js]]></title>
            <link rel="alternate" href="https://github.com/gpujs/gpu.js" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/5931"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[GPU Accelerated JavaScript.

GPU.js is a JavaScript Acceleration library for GPGPU (General purpose computing on GPUs) in JavaScript for Web and Node. GPU.js automatically transpiles simple JavaScript functions into shader language and compiles them so they run on your GPU. In case a GPU is not available, the functions will still run in regular JavaScript.]]>
            </summary>
            <updated>2025-08-29T08:25:36+00:00</updated>
        </entry>
            <entry>
            <id>https://links.biapy.com/links/6904</id>
            <title type="text"><![CDATA[Nyrna]]></title>
            <link rel="alternate" href="https://nyrna.merritt.codes/" />
            <link rel="via" type="application/atom+xml" href="https://links.biapy.com/links/6904"/>
            <author>
                <name><![CDATA[Biapy]]></name>
            </author>
            <summary type="text">
                <![CDATA[Similar to the incredibly useful sleep/suspend function found in consoles like the Nintendo Switch and Sony PlayStation; suspend your game (and its resource usage) at any time, and resume whenever you wish - at the push of a button.]]>
            </summary>
            <updated>2025-08-29T11:08:03+00:00</updated>
        </entry>
    </feed>
